GCP Vertex AI Integration with Bwat

Configure GCP Vertex AI with Bwat to access advanced AI models like Claude 3.5 Sonnet v2. This enterprise-focused guide covers secure setup, authentication, and optimal usage.

Overview

GCP Vertex AI: Google Cloud's managed AI platform providing access to cutting-edge models including Anthropic's Claude 3.5 Sonnet v2. Vertex AI Documentation (opens in a new tab)

Bwat Integration: The Bwat VS Code extension connects securely to Vertex AI, enabling AI-assisted development while maintaining enterprise security standards.

Step 1: GCP Environment Setup

1.1 Project Configuration

1. Access Google Cloud Console (opens in a new tab)
2. Create new project or select existing one for Vertex AI

1.2 IAM & Service Accounts

Assign essential roles:

• Vertex AI User (roles/aiplatform.user)
• Vertex AI Service Agent (roles/aiplatform.serviceAgent)

For cross-project resources:

• Add BigQuery Data Viewer for datasets
• Include Storage Object Viewer for Cloud Storage

Step 2: Model & Region Selection

2.1 Regional Deployment

Key supported regions:

• us-east5 (Ohio)
• us-central1 (Iowa)
• europe-west4 (Netherlands)
• asia-southeast1 (Singapore)

2.2 Model Activation

1. Navigate to Vertex AI → Model Garden
2. Locate and enable Claude 3.5 Sonnet v2

Step 3: Bwat Configuration

3.1 Extension Setup

1. Install VS Code
2. Add Bwat from Extensions Marketplace (Ctrl/Cmd+Shift+X)

3.2 Vertex AI Connection

1. Open Bwat settings (⚙️ icon)
2. Select GCP Vertex AI as provider
3. Enter:
   • Google Cloud Project ID
   • Selected region
   • Claude 3.5 Sonnet v2 model

Authentication Options

Option A: User Credentials

gcloud init
gcloud auth application-default login

Restart VS Code after authentication

Option B: Service Account

1. Create service account with required roles
2. Generate/download JSON key
3. Set environment variable:

export GOOGLE_APPLICATION_CREDENTIALS="/path/to/key.json"

Launch VS Code from same terminal session

Security & Operations

Best Practices

• Implement least privilege access
• Monitor via Model Observability dashboard
• Manage quotas in IAM & Admin > Quotas
• For cross-project resources, verify additional role assignments

Key Service Agents

• Vertex AI Service Agent
• Vertex AI RAG Data Service Agent
• Vertex AI Custom Code Service Agent

Implementation Summary

1. GCP Setup: Configure project and IAM roles
2. Model Access: Enable Claude 3.5 in selected region
3. Bwat Configuration: Install extension and connect to Vertex AI
4. Authentication: Choose user or service account method
5. Security: Apply monitoring and access controls

For ongoing updates, refer to Vertex AI docs (opens in a new tab) and internal security policies.